Anomaly detection device, machine tool, anomaly detection method, and program

ABSTRACT

An anomaly detection device of a machine tool includes a time series data acquisition section for acquiring target time series data that is the time series data of a moving load in the Z-axis direction of a cutting tool of the machine tool during a drilling process, an evaluation value derivation section for deriving an evaluation value indicating a degree of similarity between at least a part of the acquired target time series data and at least a part of reference time series data that is time series data of the moving load that can be regarded as normal using singular spectrum transformation, and an anomaly determination section for determining the presence or absence of an anomaly of the machine tool based on the derived evaluation value.

TECHNICAL FIELD

The present specification discloses an anomaly detection device, amachine tool, an anomaly detection method, and a program.

BACKGROUND ART

Conventionally, there is a device known for detecting breakage of a toolof a machine tool for performing a drilling process. For example, the NCdevice (numerical control device) described in Patent Literature 1detects a tool failure of a main shaft based on the magnitude of currentflowing through a servo motor. Specifically, the failure detectingsignal is outputted when the sensed current exceeds a set value ofcurrent corresponding to an abnormal feed load of the main shaft for apredetermined time period.

PATENT LITERATURE

-   Patent Literature 1: Japanese Laid-open Patent Publication No.    11-170105

BRIEF SUMMARY Technical Problem

However, in the method of merely determining an anomaly based on themagnitude and the set value of a current as disclosed in PatentLiterature 1, the detection accuracy of the anomaly may be insufficient.

The present disclosure has been made to solve the above-mentionedproblem, and it is a principal object of the present disclosure toaccurately detect an anomaly in a machine tool.

Solution to Problem

The present disclosure adopts a configuration, which will be describedbelow, to achieve the main object described above.

The anomaly detection device of the present disclosure is an anomalydetection device of a machine tool,

the machine tool comprising:a cutting tool configured to perform drilling,a first driving section configured to axially rotate the cutting tool,anda second driving section configured to move the cutting tool in theZ-axis direction that is the axial direction of the cutting tool; andthe anomaly detection device comprising:a time series data acquisition section for acquiring target time seriesdata that is the time series data of a moving load in the Z-axisdirection of the cutting tool during the drilling process,an evaluation value derivation section for deriving an evaluation valueindicating a degree of similarity between at least a part of theacquired target time series data andat least a part of reference time series data that is time series dataof the moving load that can be regarded as normal using singularspectrum transformation, andan anomaly determination section for determining the presence or absenceof an anomaly of the machine tool based on the derived evaluation value.

The anomaly detection device first acquires target time series data,which is time series data of the moving load in the Z-axis direction ofthe cutting tool during the drilling process. The anomaly detectiondevice next derives an evaluation value indicating the degree ofsimilarity between at least a part of the target time series data and atleast a part of the reference time series data, which is the time seriesdata of the moving load deemed to be normal, using singular spectrumtransformation (also called singular spectrum analysis). The anomalydetection device then determines whether there is an anomaly in themachine tool based on the evaluation value. By using singular spectrumtransformation, the anomaly detection device derives an evaluation valueindicating the degree of similarity between the characteristic patternsof each of the target time-series data acquired this time and thereference time-series data. Accordingly, in this anomaly detectiondevice, by determining the presence or absence of an anomaly based onthe derived evaluation value, it is possible to accurately detect ananomaly of the machine tool compared with, for example, the case wherethe anomaly is determined based merely on the magnitude of the current.An anomaly of the machine tool is, for example, breakage of a bladetool. In this case, the evaluation value may be a degree of similarityor a degree of change.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 A front view showing a schematic configuration of machine tool10.

FIG. 2 A block diagram showing electrical connections of machine tool10.

FIG. 3 A flowchart showing an example of an anomaly detection processroutine.

FIG. 4 A conceptual diagram showing a target time series matrix X₁generated from target time series data.

FIG. 5 A conceptual diagram showing a target characteristic matrix U1derived from the target time series matrix X₁.

DESCRIPTION OF EMBODIMENTS

Machine tool 10 which is an example of an anomaly detection device and amachine tool of an embodiment of the present disclosure will bedescribed below with reference to the accompanying drawings. FIG. 1 is afront view showing a schematic configuration of machine tool 10, andFIG. 2 is a block diagram showing electrical connections of machine tool10. The hatched portion in FIG. 1 is a cross-section in which guidemember 36 is cut in a plane parallel to the drawing sheet. Machine tool10 is a machine for lifting and lowering drill 26 (an example of acutting tool) to perform a drilling process on object 60 such as a metalmember. Machine tool 10 includes base 11, head 20, head moving mechanism30, current sensor 40 (see FIG. 2), Z-axis position sensor 42 (see FIG.2), light emitting section 44, and control section 50. Head 20, headmoving mechanism 30, and control section 50 are disposed on base 11.Object 60 to be drilled is placed on base 11 and directly below drill 26of head 20.

Head 20 is a device for performing a drilling process on object 60 bylifting and lowering drill 26 while axially rotating drill 26. Head 20includes head main body 21, lifting/lowering plate 22, Q-axis motor 24(one example of a first driver), and drill 26. Head body 21 is a memberhaving an approximate rectangular parallelepiped shape, and Q-axis motor24 is disposed therein. Lifting/lowering plate 22 is connected to theleft side of head main body 21. Lifting/lowering plate 22 is aplate-shaped member and is attached to ball screw 32, extending in theup-down direction, in a manner which allows lifting/lowering plate 22 togo up and down. Q-axis motor 24 outputs a rotational driving force toaxially rotate drill 26. Drill 26 is a member for performing a drillingprocess on object 60. Drill 26 is attached in an exchangeable manner tothe underside of head 20. The axial direction of drill 26 is an up-downdirection indicated by the arrow in FIG. 1. The up-down direction isalso called the Z-axis direction.

Head moving mechanism 30 is a mechanism for moving head 20 in the Z-axisdirection, that is, for lifting and lowering head 20. Head movingmechanism 30 includes ball screw 32, Z-axis motor 34 (one example of asecond driver), and guide member 36. Ball screw 32 is disposed so thatthe axial direction thereof is parallel to the Z-axis direction andpenetrates lifting/lowering plate 22 in the up-down direction. Z-axismotor 34 is configured as, for example, a servo motor, is disposed aboveball screw 32, and outputs a rotational driving force to axially rotateball screw 32. Guide member 36 is a box-shaped member having an innerspace opened to the right side in FIG. 1, and ball screw 32 andlifting/lowering plate 22 are disposed in the inner space. Guide member36 includes a guide rail (not shown) on the inner peripheral facethereof and guides the lifting and lowering of lifting/lowering plate22. Z-axis motor 34 is disposed on guide member 36. Head movingmechanism 30 moves the entire head 20 including drill 26 in the Z-axisdirection by lifting and lowering lifting/lowering plate 22 by causingZ-axis motor 34 to rotate ball screw 32.

Current sensor 40 (see FIG. 2) measures the drive current of Z-axismotor 34. The drive current of Z-axis motor 34 correlates with thetorque of the drive shaft of Z-axis motor 34 and ball screw 32, whereasthe torque of ball screw 32 correlates with the moving load in theZ-axis direction of drill 26. Therefore, the drive current of Z-axismotor 34 is information indicating the moving load of drill 26 in theZ-axis direction.

Z-axis position sensor 42 (see FIG. 2) is a sensor for sensing theposition of head 20 in the Z-axis direction. In the present embodiment,Z-axis position sensor 42 is a laser displacement type sensor attachedto head 20. Z-axis position sensor 42 irradiates laser light downward,receives the laser light after being reflected by the upper surface ofbase 11, and senses the position of head 20 in the Z-direction based onthe difference in the light receiving position of the laser light.

Light emitting section 44 is a light source unit having multiple LEDs ofeach of three colors of red, green, and blue, and can emit light invarious colors. Light emitting section 44 is disposed on the right faceof the upper end portion of guide member 36. Light emitting section 44is used, for example, to notify an operator of an anomaly.

Control section 50 is configured as a microcomputer centered on a CPU(not shown) and includes ROM for storing various programs, RAM fortemporarily storing data, an input/output port (none of which areshown), and the like in addition to the CPU. Control section 50 alsoincludes storage section 52 configured by an HDD or the like. Storagesection 52 stores reference time series data 55 described later. Controlsection 50 outputs control signals to and controls Q-axis motor 24,Z-axis motor 34, and light emitting section 44. In addition, the currentvalue of ball screw 32 outputted from current sensor 40, the positionsensing signal from Z-axis position sensor 42, and the like are receivedby control section 50.

Next, an operation when machine tool 10 performs a drilling process fordrilling object 60 will be described. Machine tool 10 performs adrilling process on object 60, for example, based on a productionprogram received from a management device (not shown), and repetitivelyexecutes the drilling process. The production program includesinformation such as the shape of object 60, the depth of the hole to bedrilled, and the number of objects 60 to be drilled. When machine tool10 performs the drilling process, object 60 is first conveyed to base 11by a conveyance device (not shown) such as a robot arm or a beltconveyor, and is positioned directly below drill 26. Control section 50of machine tool 10 next drives Q-axis motor 24 to rotate drill 26 anddrives Z-axis motor 34 to lower drill 26. Control section 50 then lowershead 20 based on the position sensing signal from Z-axis position sensor42 until a hole having a depth to be formed in object 60 is drilled.Thereafter, control section 50 causes Z-axis motor 34 to lift drill 26to retract drill 26 to an area above object 60. Thereafter, object 60subjected to a drilling process is conveyed from machine tool 10 by aconveyance device (not shown) and sent to, for example, the next step.Machine tool 10 repetitively executes such a drilling process a numberof times determined by the production program. Here, the process inwhich drill 26 actually cuts object 60 in the drilling process isreferred to as a drilling process. That is, one drilling process refersto a lowering of drill 26 until drill 26 finishes descending aftercoming in contact with object 60.

When performing the drilling process, machine tool 10 performs ananomaly detection process for detecting an anomaly of machine tool 10such as, for example, breakage of drill 26. FIG. 3 is a flowchartshowing an example of an anomaly detection process routine, FIG. 4 is aconceptual diagram showing a target time series matrix X₁ generated fromtarget time series data, and FIG. 5 is a conceptual diagram showing atarget characteristic matrix U₁ derived from a target time series matrixX₁. The anomaly detection process routine is stored in, for example,storage section 52 and is started when the drilling process starts (forexample, when lowering of drill 26 starts).

When the execution of the anomaly detection process routine is started,control section 50 first acquires target time series data, which is thetime series data of the moving load in the Z-axis direction of drill 26during the drilling process (S100). In the present embodiment, asdescribed above, the drive current of Z-axis motor 34 is used asinformation indicating the moving load in the Z-axis direction of drill26. Accordingly, in S100, control section 50 acquires the target timeseries data based on the drive current measured by current sensor 40.The waveform shown in FIG. 4 is an example of a waveform of the drivecurrent measured by current sensor 40. The “cutting period” in FIG. 4represents a period during which one drilling operation is performed. Inthe present embodiment, control section 50 acquires a waveform of thedrive current from the beginning to the end of the cutting period as thetarget time series data. Control section 50 can detect the start and endof the cutting period, for example, based on the positional informationof head 20 acquired from Z-axis position sensor 42, the height of object60 included in the production program, the depth of the hole to beformed, and the like, and acquire the target time series data.Specifically, the target time series data is, for example, a set of datain which time (or a measurement sequence) and current values areassociated with each other. Let t be the time, and let the current valueat time t be X(t). The target time series data are data of (M+N−1)current values from time T to time (T+M+N−2). M, N will be describedlater.

Subsequently, control section 50 generates a target time series matrixX₁ represented by the following expression (1) based on at least some ofthe target time series data acquired in S100 (S110).

$\begin{matrix}{X_{1} = \begin{bmatrix}{{X(T)}\mspace{95mu}} & {{X\left( {T + 1} \right)}\mspace{11mu}} & \cdots & {{X\left( {T + N - 1} \right)}\mspace{50mu}} \\{{X\left( {T + 1} \right)}\mspace{50mu}} & {{X\left( {T + 2} \right)}\mspace{11mu}} & \cdots & {{X\left( {T + N} \right)}\mspace{95mu}} \\\vdots & \vdots & \vdots & \vdots \\{X\left( {T + M - 1} \right)} & {X\left( {T + M} \right)} & \cdots & {X\left( {T + M + N - 2} \right)}\end{bmatrix}} & (1)\end{matrix}$

In the present embodiment, control section 50 generates the target timeseries matrix X₁ using all of the target time series data acquired inS100. As can be understood from the following expression (1) and FIG. 4,the target time series matrix X₁ is generated, for example, as follows.First, control section 50 extracts a partial time series (also referredto as a slide window) of M consecutive current values from time T amongthe target time series data (X(T), X(T+1), . . . , X(T+M+N−2) to form acolumn vector constituting the target time series matrix X₁. Controlsection 50 then extracts a total of N column vectors by shifting thepositions from time T to time (T+N−1) one by one and arranges them inthe column direction to obtain a target time series matrix X₁ of M rowsand N columns. As described above, control section 50 creates a matrixhaving N sets of M pieces of data by extracting multiple pieces of dataof M consecutive current values (partial time series) at different timesbased on the target time series data. The values of M and N can bedetermined in advance by experiments, for example, as values capable ofaccurately detecting an anomaly without increasing the amount of dataexcessively. It should be noted that when the number of pieces of dataof the target time series data is larger than the number (M+N−1) used togenerate the target time series matrix X₁, control section 50 maygenerate the target time series matrix X₁ using data from a part of thetarget time series data.

Next, control section 50 derives a target characteristic matrix U₁indicating a characteristic pattern (hereinafter, a characteristicpattern) of the target time series data based on the result of singularvalue decomposition of the target time series matrix X₁ generated inS110 (Step S120). In S120, control section 50 first decomposes thetarget time series matrix X₁ of M rows and N columns by singular valuesto derive a left singular matrix U_(r), a diagonal matrix of r rows andr columns, and the matrix VrT (see the upper portion of FIG. 5). Theleft singular matrix U_(r) is a matrix of M rows and r columns. Thediagonal matrix is a matrix of an r matrix and an r matrix each havinge₁, e₂, . . . , e_(r) as diagonal elements. The matrix VrT is a matrixof r rows and N columns and is a transposed matrix of the right singularmatrix Vr. r is the rank of the target time series matrix X₁. Such asingular value decomposition is well known and is described, forexample, in reference literature (Tsuyoshi Ide, “Introduction to AnomalyDetection using Machine Learning—Practical Guide using R-”, CoronaCorporation, Mar. 13, 2015). Subsequently, control section 50 derivesthe target characteristic matrix U₁ of M rows and tm columns includingthe elements of the first column to the m-th column (m is an integerthat is not greater than r) of the left singular matrix U_(r), based onthe left singular matrix U_(r) derived by the singular valuedecomposition (see the lower portion of FIG. 5). The targetcharacteristic matrix U₁ obtained in this manner is data indicating acharacteristic pattern of the target time series data (morespecifically, the target time series matrix X₁ based on the target timeseries data). Here, in the left singular matrix U_(r), the more oneprogresses from the r-th column to the first column, the more dataindicating the overall or dominant characteristic pattern of the targettime series matrix X₁. Therefore, the target characteristic matrix U₁including the elements in the first column to the m-th column of theleft singular matrix U_(r) is configured to be data indicating acharacteristic pattern useful for determining the anomaly detection inthe target time series matrix X₁ by removing the influence of elementssuch as noise of the current waveform unnecessary for the anomalydetection. The value of m can be determined in advance by experiments soas to enable accurate detection of an anomaly.

Next, in S130, control section 50 reads reference time series data 55stored in storage section 52. Reference time series data 55 is timeseries data of the moving load in the Z-axis direction of drill 26 atthe time of the drilling operation deemed to be normal. In the presentembodiment, object 60 is drilled in advance in a state where there areno anomalies in machine tool 10 such as breakage or chipping of drill26, reference time series data 55 is generated based on the drivecurrent of Z-axis motor 34 measured at this time and is stored instorage section 52.

Subsequently, control section 50 generates the reference time seriesmatrix X₂ based on the data of at least a part of reference time seriesdata 55 read in S130 (Step S140). Since S140 can be performed in thesame manner as the generation of the target time-series matrix X₁ inS110 described above, detailed descriptions thereof will be omitted. Thevalues of M and N in S140 are the same as those in S110.

Thereafter, control section 50 derives a reference characteristic matrixU₂ indicating the characteristic pattern of reference time series data55 based on the result of singular value decomposition of the referencetime series matrix X₂ generated in S140 (Step S150). Since S150 can beperformed in the same manner as the derivation of the targetcharacteristic matrix U₁ in S120 described above, detailed descriptionsthereof will be omitted. The value of m in S150 is set to the same valueas in S120. The derived reference characteristic matrix U₂ is dataindicating a characteristic pattern useful for determining anomalydetection among the reference time series matrix X₂.

Control section 50 then derives a matrix 2 norm from the matrix productof the target characteristic matrix U₁ derived in S120 and the referencecharacteristic matrix U₂ derived in S150 by the following equation (2)and sets the derived value as the similarity R (Step S160). The matrix 2norm is well known and is described, for example, in the above-mentionedreferences. The similarity R has a larger value as the characteristicpattern of the target time series data (more specifically, the targettime series matrix X₁ based on the target time series data) and thecharacteristic pattern of reference time series data 55 (morespecifically, the reference time series matrix X₂ based on referencetime series data 55) are similar to each other. Here, a method ofobtaining characteristic patterns of two sets of time series data (here,the target characteristic matrix U₁ and the reference characteristicmatrix U₂) using singular value decomposition is referred to as singularspectrum transformation. Control section 50 then derives the similarityR indicating the degree of similarity between the two (in other words,the degree of change between the two) based on the two characteristicpatterns obtained by using singular spectrum transformation. Asdescribed above, in the present embodiment, by using singular spectrumtransformation, control section 50 derives the similarity R as anevaluation value that accurately indicates the degree of similaritybetween the characteristic pattern of the target time series data andreference time series data 55, from which, for example, the influence ofnoise that causes different current waveforms every time is removed.

R=∥U ₁ ^(T) U ₂∥₂  (2)

When the similarity R is derived in S160, control section 50 determineswhether there is an anomaly in machine tool 10 based on the similarity R(Step S170). In the present embodiment, control section 50 determinesthat there is an anomaly when the similarity R is less than or equal tothe predetermined threshold value Rref. The threshold value Rref can bedetermined in advance by, for example, experiment.

If it is determined in S170 that there is an anomaly, control section 50stops the operation of machine tool 10, for example, by stopping Q-axismotor 24 and Z-axis motor 34, causes light emitting section 44 to emitlight to notify the operator of the anomaly (Step S180) and terminatesthe present routine. The notification of the anomaly may be performednot only by light emission but also by outputting a sound, or may beperformed by outputting a signal for notifying the anomaly to amanagement device of machine tool 10, a terminal owned by an operator,or the like.

On the other hand, when it is determined in S170 that there are noanomalies, control section 50 stores (in this case, overwrites) thetarget time series data acquired in S100 this time in storage section 52as reference time series data 55 (Step S190). If control section 50determines in S170 that there are no abnormalities, the target timeseries data acquired in S100 this time can be regarded as normal timeseries data. Therefore, control section 50 stores the target time seriesdata in storage section 52 so as to use the target time series data asnew reference time series data 55. As a result, when the next anomalydetection process routine is executed, control section 50 reads thetarget time series data acquired in S100 that is the latest (last time)in S130 from storage section 52 as reference time series data 55.

Here, the correspondence between configuration elements of the presentembodiment and configuration elements of the present disclosure will bespecified. Machine tool 10 of the present embodiment corresponds to amachine tool and an anomaly detection device of the present disclosure,drill 26 corresponds to a cutting tool, Q-axis motor 24 corresponds to afirst driver, Z-axis motor 34 corresponds to a second driver, andcontrol section 50 corresponds to a time series data acquisitionsection, an evaluation value derivation section, and an anomalydetermination section. In the present embodiment, an example of theanomaly detection method of the present disclosure is also disclosed bydescribing the operation of control section 50.

In machine tool 10 of the present embodiment described in detail above,control section 50 first acquires target time series data that is thetime series data of the moving load in the Z-axis direction of drill 26(in this case, the current of Z-axis motor 34) during the drillingprocess. Next, control section 50 uses singular spectrum transformationto derive an evaluation value (here, the similarity R) indicating thedegree of similarity between at least a part of the target time seriesdata and at least a part of the reference time series data 55, which isthe time series data of the current of Z-axis motor 34 deemed to benormal. Control section 50 then determines whether there is an anomalyin machine tool 10 based on the similarity R. By using the singularspectrum transformation, control section 50 can derive the similarity Rindicating the degree of similarity between the characteristic patternsof each of the target time-series data acquired this time and referencetime-series data 55. Accordingly, in machine tool 10, by determining thepresence or absence of an anomaly based on the similarity R derived bycontrol section 50, it is possible to accurately detect an anomaly ofmachine tool 10, such as a fracture of drill 26, as compared with, forexample, a case where the anomaly is determined merely based on themagnitude of the current. For example, the target time series data andthe reference time series data 55 are ideally the same data, but areactually affected by various noises and the like. Therefore, even if thetarget time series data is normal data, the target time series data andreference time series data 55 are not exactly the same. Even in such acase, in machine tool 10 of the present embodiment, by using theabove-described method, it is possible to accurately detect an anomalyof machine tool 10 while preventing erroneous detection or erroneousnon-detection of an anomaly.

In addition, since control section 50 performs S190 described above, thesimilarity R is derived using the target time series data acquired atthe time of the drilling process that is not determined to be abnormalin S170 of the anomaly detection process and was performed one time agoas reference time series data 55. Accordingly, control section 50derives the similarity R when the time series data that was last(previously) deemed as normal is used as reference time series data 55.Therefore, for example, even in a case where the time-series data at thetime of normal drilling changes with age, it is unlikely to erroneouslydetect the change with age as an anomaly. Accordingly, in machine tool10, it is possible to detect an anomaly of machine tool 10 with higheraccuracy.

As a matter of course, the present disclosure is not limited to theabove-described embodiment and may be implemented in various aspects aslong as the aspects belong within the technical scope of the presentdisclosure.

For example, in the above embodiment, in S100, control section 50acquires time series data of the drive current of Z-axis motor 34 fromthe beginning to the end of one drilling operation (cutting period).However, the present disclosure is not limited to this, and controlsection 50 may obtain the time series data of at least a part of thetime period during one drilling operation as the target time seriesdata. In the above embodiment, control section 50 generates the targettime series matrix X₁ using all of the acquired target time series data(from time T to time T to time T+M+N−2), but the present disclosure isnot limited to this, and the target time series matrix X₁ may begenerated using at least some of the acquired time series data. That is,the target time series matrix X₁ may be generated based on time seriesdata of the drive current in at least a part of the period from thebeginning to the end of one drilling operation. The same applies toreference time series data 55 and the reference time series matrix X₂.In the case where time series data of a part of the cutting period isused, it is preferable that the time T has the same value (time seriesdata of the same period among the cutting periods) between the targettime series matrix X₁ and the reference time series matrix X₂, but thetime T may be different from each other.

In this case, control section 50 need not use the time series data ofthe drive current of Z-axis motor 34 for a predetermined period on thestarting side of one drilling operation to derive the similarity R. Forexample, control section 50 need not include the time series data of thepredetermined period in the target time series data, or may include thetime series data in the target time series data but need not be used togenerate the target time series matrix X₁. As a result, the number ofpieces of data used to derive the similarity R can be reduced, thusreducing the processing load on control section 50. In addition, even ifan anomaly occurs during a predetermined period on the starting side ofone drilling operation, if the anomaly continues, the anomaly is oftenreflected in the similarity R derived using the time series data of theremaining period after the predetermined period. Therefore, even if thetime series data of the drive current for the predetermined period onthe starting side is not used to derive the similarity R, the accuracyof the anomaly detection of machine tool 10 is unlikely to be reduced.As described above, it is possible to reduce the processing burden oncontrol section 50 while suppressing deterioration of the accuracy ofthe anomaly detection of machine tool 10. The predetermined period maybe a period including the first half of one drilling operation.

In the above embodiment, although control section 50 necessarilyperforms the process of S190 when it is determined that there is noanomaly in S170, the process is not limited to this. For example,control section 50 may count the number of times it is determined thatthere is no anomaly in S170 and perform the process in S190 when thecounted number reaches a predetermined number P(>1). As a result,control section 50 uses, as reference time series data 55, the targettime series data that is relatively recent (any one of first time to Ptimes ago) for which it is determined that there is no anomaly in S170.Even in this manner, as in the above-described embodiment, it isunlikely to erroneously detect a change in the time-series data duringnormal drilling processes due to age as an anomaly. In addition,compared with the case where S190 is performed each time as in the aboveembodiment, the processing load of control section 50 can be reduced. Itshould be noted that the case where the predetermined number of times Pin this example is set to 1 corresponds to the above-describedembodiment. In addition, control section 50 may not perform S190 at all.Even in this case, by storing reference time series data 55 in advancein storage section 52, the similarity R can be derived based onreference time series data 55.

In the above embodiment, control section 50 derives the similarity R asan evaluation value indicating the degree of similarity between thetarget time series data and reference time series data 55, but thepresent disclosure is not limited to this. For example, the degree ofchange A represented by the following expression (3) may be derived asthe evaluation value. The degree of change A has a smaller value themore the characteristic pattern of the target time series data (morespecifically, the target time series matrix X₁ based on the target timeseries data) and the characteristic pattern of reference time seriesdata 55 (more specifically, the reference time series matrix X₂ based onreference time series data 55) are similar to each other. Accordingly,control section 50 may determine that there is an anomaly in machinetool 10, for example, when the degree of change A is derived in S160 andthe degree of change A exceeds a predetermined threshold ARef in S170.

A=1−(∥U ₁ ^(T) U ₂∥₂)₂  (3)

In the above embodiment, the drive current of Z-axis motor 34 is used asinformation indicating the moving load in the Z-axis direction of drill26 during the drilling process, but the present disclosure is notlimited to this. For example, the torque of the drive shaft of Z-axismotor 34 or the torque of ball screw 32 may be measured by a torquemeter, and this may be used as information indicating the moving load.

In the above embodiment, the time series data of the moving load in theZ-axis direction of drill 26 is used, but instead of this, it isconceivable to use the time series data of the load of the axialrotation of drill 26 (e.g., the torque or the current of Q-axis motor24) as the target time series data and the reference time series data.However, particularly in the case where the diameter of drill 26 issmall, the load of the axial rotation of drill 26 is likely to be smalldue to a light weight of drill 26, a small moment for rotating drill 26,a small cutting area of object 60 by drill 26, a small cuttingresistance, and the like. Then, when the load of the axial rotation ofdrill 26 is small, the size of the time series data (e.g., the size ofX(t) in FIG. 4) is small overall such that the difference between thetime series data at the time of normal operation and the time ofabnormal operation is also small, thus making it difficult to detect ananomaly. On the other hand, by using the time series data of the movingload in the Z-axis direction of drill 26 as in the present embodiment,it is possible to detect the anomaly of machine tool 10 in a stablemanner regardless of the size of the diameter of drill 26. Here, thediameter of drill 26 may be smaller than the diameter of ball screw 32.Even in this case, for the reasons described above, machine tool 10 ofthe present embodiment can accurately detect an anomaly of machine tool10.

In the above embodiment, reference time series data 55 is stored instorage section 52, but the present disclosure is not limited to this.Since the evaluation value based on reference time series data 55 needonly be derived, reference time series data 55 itself need not be storedin storage section 52. For example, at least one of the reference timeseries matrix X₂, the left singular matrix U_(r), and the referencecharacteristic matrix U₂, those being derived based on reference timeseries data 55, may be stored in storage section 52 in addition to orinstead of reference time series data 55. In this case, control section50 may change the above-described S130 or omit at least one of S140,S150 as required. Also in S190, control section 50 may store at leastone of the reference time series matrix X₂, the left singular matrixU_(r), and the reference characteristic matrix U₂ in storage section 52in addition to or instead of reference time series data 55.

In the above embodiment, control section 50 performs the anomalydetermination once in one drilling operation, but the present disclosureis not limited to this. For example, in one drilling operation, controlsection 50 may change the period acquired as the target time-series datain S100 among the cutting periods and execute the anomaly determinationprocess routine described above multiple times.

In the above embodiment, machine tool 10 performs the drilling processonce on one object 60, but the present disclosure is not limited tothis, and machine tool 10 may perform the drilling process multipletimes on one object 60. In this case, the same reference time seriesdata 55, M, N, m, and Rref may be used for multiple drilling processes.In addition, for example, appropriate reference time series data 55, M,N, m, and Rref may be used according to the processing content (forexample, the depth of the hole to be formed).

In the above embodiment, the Z-axis direction is the up-down directionin FIG. 1, but the present disclosure is not limited to this. The Z-axisdirection may be the axial direction of the cutting tool, in otherwords, the axial direction of the hole formed in object 60. For example,the Z-axis direction may be a horizontal direction such as theleft-right direction.

In the above embodiment, machine tool 10 also serves as an anomalydetection device for detecting an anomaly of machine tool 10 itself, butthe present disclosure is not limited to this. For example, a portion ofcontrol section 50 having a function of performing the anomaly detectionprocess may be an anomaly detection device independent of machine tool10. In the above embodiment, an anomaly detection device and machinetool 10 as a machine tool of the present disclosure were described, butthe present disclosure is not particularly limited to this and may be inthe form of an anomaly detection method or a program thereof.

The anomaly detection device, the machine tool, the anomaly detectionmethod, and the program of the present disclosure may be configured asfollows.

In the anomaly detection device of the present disclosure, theevaluation value derivation section may derive the evaluation value byusing the target time series data acquired during the drilling operationthat is not determined to be an anomaly by the anomaly determinationsection and performed within the latest predetermined number of times asthe reference time series data. In this way, since the anomaly detectiondevice derives the evaluation value when the time series data that canbe regarded as relatively recent normal is used as the reference timeseries data, for example, even if the time series data at the time ofnormal drilling changes with age, it is unlikely to erroneously detectthe change with age as an anomaly. Accordingly, the anomaly detectiondevice detects an anomaly of the machine tool with higher accuracy.

In this case, the predetermined number of times may be a value 1.Accordingly, since the anomaly detection device derives the evaluationvalue when the time series data that can be regarded as the latestnormal is used as reference time series data, it is possible to preventerroneous detection of changes over time as an anomaly.

In the anomaly detection device of the present disclosure, theevaluation value derivation section need not use the time series data ofthe moving load for a predetermined period on the starting side of onedrilling operation to derive the evaluation value. As a result, thenumber of pieces of data used to derive the evaluation value can bereduced, thus enabling reduction of the processing load of theevaluation value derivation section. In addition, even if an anomalyoccurs during a predetermined period on the starting side, if theanomaly continues, the anomaly is often reflected in the evaluationvalue derived using the time series data of the remaining period.Therefore, even if the time series data of the moving load for thepredetermined period on the starting side is not used to derive theevaluation value, the accuracy of the anomaly detection of the machinetool is unlikely to be reduced. As described above, it is possible toreduce the processing load of the evaluation value derivation sectionwhile suppressing degradation in the accuracy of the anomaly detectionof the machine tool. In this case, the predetermined period may be aperiod including the first half of one drilling operation.

The machine tool of the present disclosure comprises:

a cutting tool configured to perform drilling,a first driving section configured to axially rotate the cutting tool,a second driving section configured to move the cutting tool in theZ-axis direction that is the axial direction of the cutting tool,a time series data acquisition section for acquiring target time seriesdata that is the time series data of a moving load in the Z-axisdirection of the cutting tool during the drilling process,an evaluation value derivation section for deriving an evaluation valueindicating a degree of similarity between at least a part of theacquired target time series data and at least a part of reference timeseries data that is time series data of the moving load that can beregarded as normal using singular spectrum transformation, andan anomaly determination section for determining the presence or absenceof an anomaly of the machine tool based on the derived evaluation value.

Since the machine tool includes a time series data acquisition section,an evaluation value derivation section, and an anomaly determinationsection similar to those of the anomaly detection device describedabove, an effect similar to the anomaly detection device describedabove, for example, the effect of accurately detecting an anomaly of themachine tool can be obtained. In addition, the machine tool itself candetect the anomaly.

The anomaly detection method of the present disclosure is an anomalydetection method of a machine tool,

the machine tool comprising:a cutting tool configured to perform drilling,a first driving section configured to axially rotate the cutting tool,anda second driving section configured to move the cutting tool in theZ-axis direction that is the axial direction of the cutting tool; andthe anomaly detection method comprising:a time series data acquisition step of acquiring target time series datathat is the time series data of a moving load in the Z-axis direction ofthe cutting tool during the drilling process,an evaluation value derivation step of deriving an evaluation valueindicating a degree of similarity between at least a part of theacquired target time series data and at least a part of reference timeseries data that is time series data of the moving load that can beregarded as normal using singular spectrum transformation, andan anomaly determination step of determining the presence or absence ofan anomaly of the machine tool based on the derived evaluation value.

In this anomaly detection method, an anomaly of the machine tool can beaccurately detected in the same manner as the anomaly detection devicedescribed above. In this anomaly detection method, various modes of theanomaly detection device described above may be employed, or steps forachieving each function of the anomaly detection device described abovemay be added.

The program of the present disclosure causes one or more computers toexecute the anomaly detection method described above. The program may berecorded on a computer-readable recording medium (e.g., a hard disk,ROM, an FD, a CD, a DVD, or the like), or may be distributed from onecomputer to another computer via a transmission medium (a communicationnetwork such as the internet or a LAN), or may be transmitted andreceived in any other form. When the program is executed by one computeror each step is shared and executed by multiple computers, each step ofthe anomaly detection method described above is executed so that thesame operation and effect as those of the anomaly detection method areobtained.

INDUSTRIAL APPLICABILITY

The present disclosure can be used in the manufacturing industry ofmachine tools for drilling objects as well as in various industries forperforming drilling using machine tools.

REFERENCE SIGNS LIST

-   10 Machine tool, 11 Base, 20 Head, 21 Head body, 22 Lifting/lowering    plate, 24 Q-axis motor, 26 Drill, 30 Head moving mechanism, 32 Ball    screw, 34 Z-axis motor, 36 Guide member, 40 Current sensor, 42    Z-axis position sensor, 44 Light emitting section, 50 Control    section, 52 Storage section, 55 Reference time series data, 60    Object

1. An anomaly detection device of a machine tool, the machine toolcomprising: a cutting tool configured to perform drilling, a firstdriving section configured to axially rotate the cutting tool, and asecond driving section configured to move the cutting tool in the Z-axisdirection that is the axial direction of the cutting tool; and theanomaly detection device comprising: a time series data acquisitionsection for acquiring target time series data that is the time seriesdata of a moving load in the Z-axis direction of the cutting tool duringthe drilling process, an evaluation value derivation section forderiving an evaluation value indicating a degree of similarity betweenat least a part of the acquired target time series data and at least apart of reference time series data that is time series data of themoving load that can be regarded as normal using singular spectrumtransformation, and an anomaly determination section for determining thepresence or absence of an anomaly of the machine tool based on thederived evaluation value.
 2. The anomaly detection device of claim 1,wherein the evaluation value derivation section derives the evaluationvalue by using the target time series data acquired at the time of thedrilling operation, which is not determined to be an anomaly by theanomaly determination section and performed within the latestpredetermined number of times, as the reference time series data.
 3. Theanomaly detection device of claim 2, wherein the predetermined number oftimes is
 1. 4. The anomaly detection device of claim 1, wherein theevaluation value derivation section does not use time series data of themoving load for a predetermined period on the starting side of onedrilling operation to derive the evaluation value.
 5. A machine tool,comprising: a cutting tool configured to perform drilling, a firstdriving section configured to axially rotate the cutting tool, a seconddriving section configured to move the cutting tool in the Z-axisdirection that is the axial direction of the cutting tool, a time seriesdata acquisition section for acquiring target time series data that isthe time series data of a moving load in the Z-axis direction of thecutting tool during the drilling process, an evaluation value derivationsection for deriving an evaluation value indicating a degree ofsimilarity between at least a part of the acquired target time seriesdata and at least a part of reference time series data that is timeseries data of the moving load that can be regarded as normal usingsingular spectrum transformation, and an anomaly determination sectionfor determining the presence or absence of an anomaly of the machinetool based on the derived evaluation value.
 6. An anomaly detectionmethod of a machine tool, the machine tool comprising: a cutting toolconfigured to perform drilling, a first driving section configured toaxially rotate the cutting tool, and a second driving section configuredto move the cutting tool in the Z-axis direction that is the axialdirection of the cutting tool; and the anomaly detection methodcomprising: a time series data acquisition step of acquiring target timeseries data that is the time series data of a moving load in the Z-axisdirection of the cutting tool during the drilling process, an evaluationvalue derivation step of deriving an evaluation value indicating adegree of similarity between at least a part of the acquired target timeseries data and at least a part of reference time series data that istime series data of the moving load that can be regarded as normal usingsingular spectrum transformation, and an anomaly determination step ofdetermining the presence or absence of an anomaly of the machine toolbased on the derived evaluation value.
 7. A program for causing one ormore computers to execute the anomaly detection method of claim 6.